Paper Citation Record · LEDGER

Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.16248.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2403.16248 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:25:14.889499Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T10:33:18.386577Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 85321a6d-80cc-4e59-b49d-c453de800edf · inbound

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows cites this paper.

TableVault: Managing Dynamic Data Collections for LLM-Augmented Workflows Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:25:14.889499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a02d49-ebab-4af1-9a4b-6f297898b859 · inbound

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry cites this paper.

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T10:06:23.655340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc02d921-93e9-417d-8d65-1d4e02f69017 · inbound

Investigating Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs cites this paper.

Investigating Notable Metadata Practices in PyPI Libraries: An Empirical Study about Repository and Donation Platform URLs Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:07:50.594554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e0abb44-c04d-48d1-b5a4-5cfb1bb15554 · inbound

Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest cites this paper.

Assessing Capabilities of Large Language Models in Social Media Analytics: A Multi-task Quest Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:41:01.758837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d903c36-398a-47a5-98da-43adbe011378 · inbound

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges cites this paper.

An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-29T10:33:18.388259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 302aaa55-7b18-4504-af34-a9b27ff400ad · inbound

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities cites this paper.

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-11T23:15:06.472205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.